The present embodiments relate to classifying anatomy. In particular, a machine-learnt classifier is used for medical imaging.
A wealth of data is contained in medical images, such as three-dimensional (3D) and four-dimensional (4D) computed tomography (CT) and Dyna-CT volumes of the chest. Manual assessment of such high-resolution datasets is clinically infeasible due to the large content and time constraints on physicians. Therefore, applications involving automatic and semi-automatic processing extract information from the datasets. Such applications include nodule detection, guidance for biopsies, categorization and detection of inflammation, and cancer staging. These applications involve anatomical understanding via segmentation. The segmentation results are the basis for further analysis and results. A robust segmentation with minimal user interaction is useful for further automated analysis.
Segmentation and identification of the airways and other structures of the lungs have been proposed by region growing, morphology, fast marching, and machine learning (ML) approaches. ML approaches use manually defined features. For example, in performing fissure detection, a Hessian operator creates a series of second order derivatives that can be used as features for a ML approach. The ML approach is applied across the entire object of interest. ML is used on derived features across multiple scales. For each scale, the same technique is applied. This multi-scale approach is used in vessel segmentation as well. However, use of multiple scales requires more training data, detailed labeled data, a complex feature set, and/or more processing as compared to ML at one scale.